1
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Cankara F, Senyuz S, Sayin AZ, Gursoy A, Keskin O. DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces. J Chem Inf Model 2024; 64:5041-5051. [PMID: 38907989 DOI: 10.1021/acs.jcim.3c01905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.
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Affiliation(s)
- Fatma Cankara
- Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey
| | - Simge Senyuz
- Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey
| | - Ahenk Zeynep Sayin
- Department of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koç University, İstanbul 34450, Turkey
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koç University, İstanbul 34450, Turkey
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2
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Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024; 52:W238-W247. [PMID: 38709873 PMCID: PMC11223847 DOI: 10.1093/nar/gkae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
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Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
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3
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Zhang Z, Zhao L, Gao M, Chen Y, Wang J, Wang C. PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training. Comput Biol Med 2024; 172:108287. [PMID: 38503089 DOI: 10.1016/j.compbiomed.2024.108287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/21/2024] [Accepted: 03/12/2024] [Indexed: 03/21/2024]
Abstract
Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.
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Affiliation(s)
- Zitong Zhang
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China
| | - Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China
| | - Mengyao Gao
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China
| | - Yuanlong Chen
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.
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4
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Xie X, Yu T, Li X, Zhang N, Foster LJ, Peng C, Huang W, He G. Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials. Signal Transduct Target Ther 2023; 8:335. [PMID: 37669923 PMCID: PMC10480221 DOI: 10.1038/s41392-023-01589-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/22/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023] Open
Abstract
Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.
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Affiliation(s)
- Xin Xie
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Tingting Yu
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China
| | - Xiang Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China
| | - Nan Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China
- Department of Dermatology and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Chengdu, China
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Cheng Peng
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China.
| | - Wei Huang
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Medical Technology and School of Pharmacy, Chengdu University of Traditional Chinese Medicine, 611137, Chengdu, China.
| | - Gu He
- Department of Dermatology and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Chengdu, China.
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5
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Gao M, Zhao L, Zhang Z, Wang J, Wang C. Using a stacked ensemble learning framework to predict modulators of protein-protein interactions. Comput Biol Med 2023; 161:107032. [PMID: 37230018 DOI: 10.1016/j.compbiomed.2023.107032] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/13/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.
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Affiliation(s)
- Mengyao Gao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
| | - Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
| | - Zitong Zhang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
| | - Junjie Wang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China.
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6
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Wolk O, Goldblum A. Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions. J Chem Inf Model 2023; 63:126-137. [PMID: 36512704 DOI: 10.1021/acs.jcim.2c00920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.
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Affiliation(s)
- Omri Wolk
- Molecular Modeling Laboratory, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Amiram Goldblum
- Molecular Modeling Laboratory, Institute for Drug Research, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
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7
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Ikeda K, Maezawa Y, Yonezawa T, Shimizu Y, Tashiro T, Kanai S, Sugaya N, Masuda Y, Inoue N, Niimi T, Masuya K, Mizuguchi K, Furuya T, Osawa M. DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions. Front Chem 2023; 10:1090643. [PMID: 36700083 PMCID: PMC9868583 DOI: 10.3389/fchem.2022.1090643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 12/09/2022] [Indexed: 01/11/2023] Open
Abstract
Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).
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Affiliation(s)
- Kazuyoshi Ikeda
- HPC—and AI-driven Drug Development Platform Division, Center for Computational Science, Yokohama, Kanagawa, Japan,Division of Physics for Life Functions, Keio University Faculty of Pharmacy, Tokyo, Japan,*Correspondence: Kazuyoshi Ikeda,
| | | | - Tomoki Yonezawa
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Yugo Shimizu
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy, Tokyo, Japan
| | | | | | | | | | - Naoko Inoue
- PeptiDream Inc., Chiyoda-Ku, Kanagawa, Japan
| | | | | | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan,Institute for Protein Research, Osaka University, Osaka, Japan
| | | | - Masanori Osawa
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy, Tokyo, Japan
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8
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Machine Learning Models to Predict Protein-Protein Interaction Inhibitors. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227986. [PMID: 36432086 PMCID: PMC9694076 DOI: 10.3390/molecules27227986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/09/2022] [Accepted: 11/16/2022] [Indexed: 11/19/2022]
Abstract
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.
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9
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Mayol GF, Defelipe LA, Arcon JP, Turjanski AG, Marti MA. Solvent Sites Improve Docking Performance of Protein–Protein Complexes and Protein–Protein Interface-Targeted Drugs. J Chem Inf Model 2022; 62:3577-3588. [DOI: 10.1021/acs.jcim.2c00264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gonzalo F. Mayol
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Lucas A. Defelipe
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
- European Molecular Biology Laboratory - Hamburg Unit, Notkestrasse 85, Hamburg 22607, Germany
| | - Juan Pablo Arcon
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
- Institute for Research in Biomedicine (IRB), 08028 Barcelona, Spain
- The Barcelona Institute of Science and Technology, 08036 Barcelona, Spain
| | - Adrian G. Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
| | - Marcelo A. Marti
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Argentina
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10
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The Nucleoside Adenosine Inhibits Intracellular Microvascular α2C-Adrenoceptor Surface Trafficking. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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11
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In Silico Prediction of Plasmodium falciparum Cytoadherence Inhibitors That Disrupt Interaction between gC1qR-DBLβ12 Complex. Pharmaceuticals (Basel) 2022; 15:ph15060691. [PMID: 35745611 PMCID: PMC9230678 DOI: 10.3390/ph15060691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 02/06/2023] Open
Abstract
Malaria causes about half a million deaths per year, mainly in children below 5 years of age. Cytoadherence of Plasmodium falciparum infected erythrocytes in brain and placenta has been linked to severe malaria and malarial related deaths. Cytoadherence is mediated by binding of human receptor gC1qR to the DBLβ12 domain of a P. falciparum erythrocyte membrane protein family 1 (PfEMP1) protein. In the present work, molecular dynamic simulation was extensively studied for the gC1qR-DBLβ12 complex. The stabilized protein complex was used to study the protein–protein interface interactions and mapping of interactive amino acid residues as hotspot were performed. Prediction of inhibitors were performed by using virtual protein–protein inhibitor database Timbal screening of about 15,000 compounds. In silico mutagenesis studies, binding profile and protein ligand interaction fingerprinting were used to strengthen the screening of the potential inhibitors of gC1qR-DBLβ12 interface. Six compounds were selected and were further subjected to the MAIP analysis and ADMET studies. From these six compounds, the compounds 3, 5, and 6 were found to outperform on all screening criteria from the rest selected compounds. These compounds may provide novel drugs to treat and manage severe falciparum malaria. Additionally. the identified hotspots can be used in future for designing novel interventions for disruption of interface interactions, such as through peptides or vaccines. Futher in vitro and in vivo studies are required for the confirmation of these compounds as potential inhibitors of gC1qR-DBLβ12 interaction.
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12
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Rodrigues CHM, Pires DEV, Ascher DB. pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors. J Chem Inf Model 2021; 61:5438-5445. [PMID: 34719929 DOI: 10.1021/acs.jcim.1c01135] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.
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Affiliation(s)
- Carlos H M Rodrigues
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia
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13
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Exploring the chemical space of protein-protein interaction inhibitors through machine learning. Sci Rep 2021; 11:13369. [PMID: 34183730 PMCID: PMC8238997 DOI: 10.1038/s41598-021-92825-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 06/16/2021] [Indexed: 11/08/2022] Open
Abstract
Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.
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Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning. Sci Rep 2021; 11:7420. [PMID: 33795749 PMCID: PMC8016952 DOI: 10.1038/s41598-021-86616-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/11/2021] [Indexed: 11/14/2022] Open
Abstract
Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56–1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.
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15
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Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective. Pharmacol Res 2021; 167:105577. [PMID: 33774182 DOI: 10.1016/j.phrs.2021.105577] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/23/2021] [Accepted: 03/21/2021] [Indexed: 12/11/2022]
Abstract
The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.
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16
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Wang C, Kurgan L. Survey of Similarity-Based Prediction of Drug-Protein Interactions. Curr Med Chem 2021; 27:5856-5886. [PMID: 31393241 DOI: 10.2174/0929867326666190808154841] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/16/2018] [Accepted: 10/23/2018] [Indexed: 12/20/2022]
Abstract
Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.
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Affiliation(s)
- Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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17
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Torchet R, Druart K, Ruano LC, Moine-Franel A, Borges H, Doppelt-Azeroual O, Brancotte B, Mareuil F, Nilges M, Ménager H, Sperandio O. The iPPI-DB initiative: A Community-centered database of Protein-Protein Interaction modulators. Bioinformatics 2021; 37:89-96. [PMID: 33416858 PMCID: PMC8034526 DOI: 10.1093/bioinformatics/btaa1091] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/25/2020] [Accepted: 12/23/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely-accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. RESULTS Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets, and extended our focus to stabilizers of PPIs as well. AVAILABILITY AND IMPLEMENTATION The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rachel Torchet
- Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Karen Druart
- Department of Structural Biology and Chemistry, Institut Pasteur, Paris, 75015, France
| | - Luis Checa Ruano
- Department of Structural Biology and Chemistry, Institut Pasteur, Paris, 75015, France
| | | | - Hélène Borges
- Department of Structural Biology and Chemistry, Institut Pasteur, Paris, 75015, France
| | - Olivia Doppelt-Azeroual
- Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Bryan Brancotte
- Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Fabien Mareuil
- Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Michael Nilges
- Department of Structural Biology and Chemistry, Institut Pasteur, Paris, 75015, France
| | - Hervé Ménager
- Hub de Bioinformatique et Biostatistique-Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, Paris, France
| | - Olivier Sperandio
- Department of Structural Biology and Chemistry, Institut Pasteur, Paris, 75015, France
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18
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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Lee J, Lee D, Lee KH. Literature mining for context-specific molecular relations using multimodal representations (COMMODAR). BMC Bioinformatics 2020; 21:250. [PMID: 33106154 PMCID: PMC7586695 DOI: 10.1186/s12859-020-3396-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 02/06/2020] [Indexed: 01/14/2023] Open
Abstract
Biological contextual information helps understand various phenomena occurring in the biological systems consisting of complex molecular relations. The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations. The main idea of COMMODAR is the feature augmentation by the cooperation of multimodal representations for relation extraction. We leveraged biomedical domain knowledge as well as canonical linguistic information for more comprehensive representations of textual sources. The models based on multiple modalities outperformed those solely based on the linguistic modality. We applied COMMODAR to the 14 million PubMed abstracts and extracted 9214 context-specific molecular relations. All corpora, extracted data, evaluation results, and the implementation code are downloadable at https://github.com/jae-hyun-lee/commodar . CCS CONCEPTS: • Computing methodologies~Information extraction • Computing methodologies~Neural networks • Applied computing~Biological networks.
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Affiliation(s)
- Jaehyun Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. .,Bio-Synergy Research Center, Daejeon, South Korea.
| | - Kwang Hyung Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
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20
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Shehroz M, Zaheer T, Hussain T. Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment. Heliyon 2020; 6:e05278. [PMID: 33083627 PMCID: PMC7561340 DOI: 10.1016/j.heliyon.2020.e05278] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/26/2020] [Accepted: 10/13/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND SARS-CoV-2 has the Spike glycoprotein (S) which is crucial in attachment with host receptor and cell entry leading to COVID-19 infection. The current study was conducted to explore drugs against Receptor Binding Domain (RBD) of SARS-CoV-2 using in silico pharmacophore modelling and virtual screening approach to combat COVID-19. METHODS All the available sequences of RBD in NCBI were retrieved and multiple aligned to get insight into its diversity. The 3D structure of RBD was modelled and the conserved region was used as a template to design pharmacophore using LigandScout. Lead compounds were screened using Cambridge, Drugbank, ZINC and TIMBLE databases and these identified lead compounds were screened for their toxicity and Lipinski's rule of five. Molecular docking of shortlisted lead compounds was performed using AutoDock Vina and interacting residues were visualized. RESULTS Active residues of Receptor Binding Motif (RBM) in S, involved in interaction with receptor, were found to be conserved in all 483 sequences. Using this RBM motif as a pharmacophore a total of 1327 lead compounds were predicted initially from all databases, however, only eight molecules fit the criteria for safe oral drugs. Conclusion: The RBM region of S interacts with Angiotensin Converting Enzyme 2 (ACE2) receptor and Glucose Regulated Protein 78 (GRP78) to mediate viral entry. Based on in silico analysis, the lead compounds scrutinized herewith interact with S, hence, can prevent its internalization in cell using ACE2 and GRP78 receptor.The compounds predicted in this study are based on rigorous computational analysis and the evaluation of predicted lead compounds can be promising in experimental studies.
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Affiliation(s)
- Muhammad Shehroz
- Department of Biotechnology, Virtual University of Pakistan, Peshawar, Pakistan
| | - Tahreem Zaheer
- Atta ur Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Tanveer Hussain
- Department of Molecular Biology, Virtual University of Pakistan, Lahore, Pakistan
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21
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Mizukoshi Y, Takeuchi K, Tokunaga Y, Matsuo H, Imai M, Fujisaki M, Kamoshida H, Takizawa T, Hanzawa H, Shimada I. Targeting the cryptic sites: NMR-based strategy to improve protein druggability by controlling the conformational equilibrium. SCIENCE ADVANCES 2020; 6:eabd0480. [PMID: 32998885 PMCID: PMC7527212 DOI: 10.1126/sciadv.abd0480] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/06/2020] [Indexed: 06/11/2023]
Abstract
Cryptic ligand binding sites, which are not evident in the unligated structures, are beneficial in tackling with difficult but attractive drug targets, such as protein-protein interactions (PPIs). However, cryptic sites have thus far not been rationally pursued in the early stages of drug development. Here, we demonstrated by nuclear magnetic resonance that the cryptic site in Bcl-xL exists in a conformational equilibrium between the open and closed conformations under the unligated condition. While the fraction of the open conformation in the unligated wild-type Bcl-xL is estimated to be low, F143W mutation that is distal from the ligand binding site can substantially elevate the population. The F143W mutant showed a higher hit rate in a phage-display peptide screening, and the hit peptide bound to the cryptic site of the wild-type Bcl-xL. Therefore, by controlling the conformational equilibrium in the cryptic site, the opportunity to identify a PPI inhibitor could be improved.
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Affiliation(s)
| | - Koh Takeuchi
- National Institute of Advanced Industrial Science and Technology (AIST), Molecular Profiling Research Center for Drug Discovery (molprof) and Cellular and Molecular Biotechnology Research Institute, Tokyo 135-0063, Japan.
| | - Yuji Tokunaga
- National Institute of Advanced Industrial Science and Technology (AIST), Molecular Profiling Research Center for Drug Discovery (molprof) and Cellular and Molecular Biotechnology Research Institute, Tokyo 135-0063, Japan
| | - Hitomi Matsuo
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | - Misaki Imai
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | - Miwa Fujisaki
- Japan Biological Informatics Consortium, Tokyo 135-0063, Japan
| | | | | | | | - Ichio Shimada
- Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.
- RIKEN, Center for Biosystems Dynamics Research, Yokohama 230-0045, Japan
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22
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Bosc N, Muller C, Hoffer L, Lagorce D, Bourg S, Derviaux C, Gourdel ME, Rain JC, Miller TW, Villoutreix BO, Miteva MA, Bonnet P, Morelli X, Sperandio O, Roche P. Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions. ACS Chem Biol 2020; 15:1566-1574. [PMID: 32320205 PMCID: PMC7399473 DOI: 10.1021/acschembio.0c00179] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.
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Affiliation(s)
- Nicolas Bosc
- Inserm U973 MTi, 25 rue Hélène Brion 75013 Paris
- Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR3528, 28 rue du Dr Roux 75015 Paris
| | - Christophe Muller
- IPC Drug Discovery Platform, Institut Paoli-Calmettes, 232 Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Laurent Hoffer
- CRCM, CNRS, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, 13009 Marseille, France
| | - David Lagorce
- Université de Paris, INSERM US14, Plateforme Maladies Rares - Orphanet, 75014 Paris, France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA), Université d’Orléans, UMR CNRS 7311, BP 6759, 45067 Orléans. France
| | - Carine Derviaux
- IPC Drug Discovery Platform, Institut Paoli-Calmettes, 232 Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Marie-Edith Gourdel
- Hybrigenics Services SAS, 1 rue Pierre Fontaine, 91000 Evry Courcouronnes, France
| | - Jean-Christophe Rain
- Hybrigenics Services SAS, 1 rue Pierre Fontaine, 91000 Evry Courcouronnes, France
| | - Thomas W. Miller
- IPC Drug Discovery Platform, Institut Paoli-Calmettes, 232 Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Bruno O. Villoutreix
- Université de Lille, INSERM, Institut Pasteur de Lille, U1177 - Drugs and Molecules for living Systems, 59000 Lille, France
| | - Maria A. Miteva
- Inserm U1268 MCTR, CNRS UMR 8038 CiTCoM – Univ. De Paris, Faculté de Pharmacie de Paris, 75006 Paris, France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA), Université d’Orléans, UMR CNRS 7311, BP 6759, 45067 Orléans. France
| | - Xavier Morelli
- IPC Drug Discovery Platform, Institut Paoli-Calmettes, 232 Boulevard de Sainte-Marguerite, 13009, Marseille, France
- CRCM, CNRS, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, 13009 Marseille, France
| | - Olivier Sperandio
- Inserm U973 MTi, 25 rue Hélène Brion 75013 Paris
- Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR3528, 28 rue du Dr Roux 75015 Paris
| | - Philippe Roche
- CRCM, CNRS, INSERM, Institut Paoli-Calmettes, Aix-Marseille Univ, 13009 Marseille, France
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23
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Cheng SS, Yang GJ, Wang W, Leung CH, Ma DL. The design and development of covalent protein-protein interaction inhibitors for cancer treatment. J Hematol Oncol 2020; 13:26. [PMID: 32228680 PMCID: PMC7106679 DOI: 10.1186/s13045-020-00850-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 02/20/2020] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.
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Affiliation(s)
- Sha-Sha Cheng
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China
| | - Guan-Jun Yang
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China
| | - Wanhe Wang
- Department of Chemistry, Hong Kong Baptist University, Kowloon, 999077, Hong Kong, China.,Institute of Medical Research, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Chung-Hang Leung
- Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China.
| | - Dik-Lung Ma
- Department of Chemistry, Hong Kong Baptist University, Kowloon, 999077, Hong Kong, China.
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24
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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25
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Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein–protein interaction inhibitors. Drug Discov Today 2019; 24:551-559. [DOI: 10.1016/j.drudis.2018.11.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/03/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022]
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26
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Si Y, Xu D, Bum-Erdene K, Ghozayel MK, Yang B, Clemons PA, Meroueh SO. Chemical Space Overlap with Critical Protein-Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries. ChemMedChem 2019; 14:119-131. [PMID: 30548204 PMCID: PMC7175409 DOI: 10.1002/cmdc.201800537] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/29/2018] [Indexed: 12/14/2022]
Abstract
There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein-protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as "hot spots". Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the "screenable chemical universe based on intuitive data organization", SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR⋅uPA, TEAD4⋅Yap1, and CaV α⋅CaV β. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR⋅uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4⋅Yap1. Virtual screening of conformationally restricted compounds targeting uPAR⋅uPA and TEAD4⋅Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.
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Affiliation(s)
- Yubing Si
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - David Xu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Khuchtumur Bum-Erdene
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Mona K Ghozayel
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Baocheng Yang
- Henan Provincial Key Laboratory of Nanocomposites and Applications, Institute of Nanostructured Functional Materials, Huanghe Science and Technology College, Zhengzhou, Henan, 450006, China
| | - Paul A Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Samy O Meroueh
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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Ghanakota P, van Vlijmen H, Sherman W, Beuming T. Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces. J Chem Inf Model 2018; 58:784-793. [PMID: 29617116 DOI: 10.1021/acs.jcim.7b00487] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Phani Ghanakota
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | | | - Woody Sherman
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Thijs Beuming
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
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29
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Alihodžić S, Bukvić M, Elenkov IJ, Hutinec A, Koštrun S, Pešić D, Saxty G, Tomašković L, Žiher D. Current Trends in Macrocyclic Drug Discovery and beyond -Ro5. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:113-233. [DOI: 10.1016/bs.pmch.2018.01.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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30
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Shin WH, Christoffer CW, Kihara D. In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 2017; 131:22-32. [PMID: 28802714 PMCID: PMC5683929 DOI: 10.1016/j.ymeth.2017.08.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 08/08/2017] [Accepted: 08/08/2017] [Indexed: 02/07/2023] Open
Abstract
A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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31
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Jin X, Lee K, Kim NH, Kim HS, Yook JI, Choi J, No KT. Natural products used as a chemical library for protein-protein interaction targeted drug discovery. J Mol Graph Model 2017; 79:46-58. [PMID: 29136547 DOI: 10.1016/j.jmgm.2017.10.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/16/2017] [Accepted: 10/17/2017] [Indexed: 12/01/2022]
Abstract
Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery.
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Affiliation(s)
- Xuemei Jin
- Department of Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Kyungro Lee
- Bioinformatics & Molecular Design Research Center (BMDRC), Yonsei University, Seoul 03722, Korea
| | - Nam Hee Kim
- Department of Oral Pathology, Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul 03722, Korea
| | - Hyun Sil Kim
- Department of Oral Pathology, Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul 03722, Korea
| | - Jong In Yook
- Department of Oral Pathology, Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul 03722, Korea
| | - Jiwon Choi
- Bioinformatics & Molecular Design Research Center (BMDRC), Yonsei University, Seoul 03722, Korea.
| | - Kyoung Tai No
- Department of Biotechnology, Yonsei University, Seoul 03722, Korea; Bioinformatics & Molecular Design Research Center (BMDRC), Yonsei University, Seoul 03722, Korea.
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32
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Bosc N, Kuenemann MA, Bécot J, Vavrusa M, Cerdan AH, Sperandio O. Privileged Substructures to Modulate Protein-Protein Interactions. J Chem Inf Model 2017; 57:2448-2462. [PMID: 28922596 DOI: 10.1021/acs.jcim.7b00435] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.
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Affiliation(s)
- Nicolas Bosc
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France.,Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France.,CNRS UMR3528, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France
| | - Mélaine A Kuenemann
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Jerome Bécot
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Marek Vavrusa
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Adrien H Cerdan
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France
| | - Olivier Sperandio
- Inserm, U973 , Paris 75013, France.,Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm , Paris 75013, France.,Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France.,CNRS UMR3528, Institut Pasteur , 25-28 rue du Dr Roux, Paris 75015, France
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33
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Lagorce D, Douguet D, Miteva MA, Villoutreix BO. Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci Rep 2017; 7:46277. [PMID: 28397808 PMCID: PMC5387685 DOI: 10.1038/srep46277] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 03/13/2017] [Indexed: 12/18/2022] Open
Abstract
The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.
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Affiliation(s)
- David Lagorce
- INSERM, U973, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Dominique Douguet
- CNRS UMR7275, Institut de Pharmacologie Moléculaire et Cellulaire, Université Côte d’Azur, Valbonne, France
| | - Maria A. Miteva
- INSERM, U973, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
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34
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Milhas S, Raux B, Betzi S, Derviaux C, Roche P, Restouin A, Basse MJ, Rebuffet E, Lugari A, Badol M, Kashyap R, Lissitzky JC, Eydoux C, Hamon V, Gourdel ME, Combes S, Zimmermann P, Aurrand-Lions M, Roux T, Rogers C, Müller S, Knapp S, Trinquet E, Collette Y, Guillemot JC, Morelli X. Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery. ACS Chem Biol 2016; 11:2140-8. [PMID: 27219844 DOI: 10.1021/acschembio.6b00286] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.
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Affiliation(s)
- Sabine Milhas
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Brigitt Raux
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Stéphane Betzi
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Carine Derviaux
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Philippe Roche
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Audrey Restouin
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Marie-Jeanne Basse
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Etienne Rebuffet
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Adrien Lugari
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Marion Badol
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Rudra Kashyap
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- Department of Human Genetics, KU Leuven, B-3000 Leuven, Belgium
| | - Jean-Claude Lissitzky
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Cécilia Eydoux
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Véronique Hamon
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | | | - Sébastien Combes
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Pascale Zimmermann
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- Department of Human Genetics, KU Leuven, B-3000 Leuven, Belgium
| | - Michel Aurrand-Lions
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Thomas Roux
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Catherine Rogers
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
| | - Susanne Müller
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
| | - Stefan Knapp
- Target
Discovery Institute, University of Oxford, NDM Research Building, Roosevelt
Drive, Oxford OX3 7FZ, U.K
- Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, U.K
- Goethe-University, Institute
for Pharmaceutical Chemistry and Buchmann
Institute for Life Science, Campus Riedberg, Max-von Laue Str. 9, 60438 Frankfurt am Main, Germany
| | - Eric Trinquet
- Cisbio Bioassays, R&D, Parc Marcel Boiteux, BP 84175, 30200 Codolet, France
| | - Yves Collette
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
| | - Jean-Claude Guillemot
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
| | - Xavier Morelli
- CNRS, INSERM, Aix-Marseille Université, Institut Paoli-Calmettes, Centre de Recherche en Cancérologie de Marseille, CS30059, 13273 Marseille Cedex 9, France
- CNRS, Aix-Marseille
Université, Screening Platform AD2P, AFMB UMR7257, 13288, Marseille, France
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35
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Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors. Sci Rep 2016; 6:23815. [PMID: 27034268 PMCID: PMC4817116 DOI: 10.1038/srep23815] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 03/15/2016] [Indexed: 02/03/2023] Open
Abstract
Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.
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36
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Basse MJ, Betzi S, Morelli X, Roche P. 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw007. [PMID: 26980515 PMCID: PMC4792518 DOI: 10.1093/database/baw007] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 01/13/2016] [Indexed: 11/30/2022]
Abstract
2P2Idb is a hand-curated structural database dedicated to protein–protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i.e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein–protein and protein–inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein–protein complexes and 274 protein–inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein–protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers. A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein–ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein–protein interactions and the design of protein–protein interaction modulators. Database URL:http://2p2idb.cnrs-mrs.fr
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Affiliation(s)
- Marie-Jeanne Basse
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Stéphane Betzi
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Xavier Morelli
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
| | - Philippe Roche
- Centre de Recherche en Cancérologie de Marseille (CRCM); CNRS, UMR 7258; INSERM U1068; Institut Paoli-Calmettes; Aix-Marseille Université; Marseille 13009, France
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Johnson DK, Karanicolas J. Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions. J Chem Inf Model 2016; 56:399-411. [PMID: 26726827 DOI: 10.1021/acs.jcim.5b00572] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.
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Affiliation(s)
- David K Johnson
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - John Karanicolas
- Center for Computational Biology, and ‡Department of Molecular Biosciences, University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
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Bakail M, Ochsenbein F. Targeting protein–protein interactions, a wide open field for drug design. CR CHIM 2016. [DOI: 10.1016/j.crci.2015.12.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces. PLoS One 2015; 10:e0140965. [PMID: 26517868 PMCID: PMC4627654 DOI: 10.1371/journal.pone.0140965] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 10/03/2015] [Indexed: 11/19/2022] Open
Abstract
Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.
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Labbé CM, Kuenemann MA, Zarzycka B, Vriend G, Nicolaes GAF, Lagorce D, Miteva MA, Villoutreix BO, Sperandio O. iPPI-DB: an online database of modulators of protein-protein interactions. Nucleic Acids Res 2015; 44:D542-7. [PMID: 26432833 PMCID: PMC4702945 DOI: 10.1093/nar/gkv982] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Accepted: 09/19/2015] [Indexed: 01/13/2023] Open
Abstract
In order to boost the identification of low-molecular-weight drugs on protein–protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein–protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.
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Affiliation(s)
- Céline M Labbé
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Mélaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Barbara Zarzycka
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Gerry A F Nicolaes
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques, In Silico, INSERM UMR-S 973, Paris, France INSERM, U973, Paris, France
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Small Molecule Targeting of Protein-Protein Interactions through Allosteric Modulation of Dynamics. Molecules 2015; 20:16435-45. [PMID: 26378508 PMCID: PMC6332300 DOI: 10.3390/molecules200916435] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 09/06/2015] [Accepted: 09/08/2015] [Indexed: 11/16/2022] Open
Abstract
The protein–protein interaction (PPI) target class is particularly challenging, but offers potential for “first in class” therapies. Most known PPI small molecules are orthosteric inhibitors but many PPI sites may be fundamentally intractable to this approach. One potential alternative is to consider more attractive, remote small molecule pockets; however, on the whole, allostery is poorly understood and difficult to discover and develop. Here we review the literature in order to understand the basis for allostery, especially as it can apply to PPIs. We suggest that the upfront generation of sophisticated and experimentally validated dynamic models of target proteins can aid in target choice and strategy for allosteric intervention to produce the required functional effect.
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Gowthaman R, Miller SA, Rogers S, Khowsathit J, Lan L, Bai N, Johnson DK, Liu C, Xu L, Anbanandam A, Aubé J, Roy A, Karanicolas J. DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites. J Med Chem 2015; 59:4152-70. [PMID: 26126123 DOI: 10.1021/acs.jmedchem.5b00150] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 μM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.
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Affiliation(s)
- Ragul Gowthaman
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Sven A Miller
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Steven Rogers
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Jittasak Khowsathit
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Lan Lan
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Nan Bai
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - David K Johnson
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Chunjing Liu
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Liang Xu
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Asokan Anbanandam
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Jeffrey Aubé
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - Anuradha Roy
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
| | - John Karanicolas
- Center for Computational Biology, ‡Department of Molecular Biosciences, §Center of Biomedical Research Excellence, Center for Cancer Experimental Therapeutics, ∥Department of Radiation Oncology, ⊥Biomolecular NMR Laboratory, #Department of Medicinal Chemistry, and ∇High Throughput Screening Laboratory University of Kansas , 2030 Becker Drive, Lawrence, Kansas 66045-7534, United States
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Metri R, Hariharaputran S, Ramakrishnan G, Anand P, Raghavender US, Ochoa-Montaño B, Higueruelo AP, Sowdhamini R, Chandra NR, Blundell TL, Srinivasan N. SInCRe-structural interactome computational resource for Mycobacterium tuberculosis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav060. [PMID: 26130660 PMCID: PMC4485431 DOI: 10.1093/database/bav060] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 05/26/2015] [Indexed: 11/20/2022]
Abstract
We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding. Database URL:http://proline.biochem.iisc.ernet.in/sincre
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Affiliation(s)
- Rahul Metri
- Department of Biochemistry and Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India
| | - Sridhar Hariharaputran
- Department of Biochemistry and National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary Road, Bangalore, India
| | - Gayatri Ramakrishnan
- Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India, and
| | | | | | | | - Alicia P Higueruelo
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, UK
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary Road, Bangalore, India
| | | | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, UK
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44
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Sperandio O, Villoutreix BO, Morelli X, Roche P. [Chemical libraries dedicated to protein-protein interactions]. Med Sci (Paris) 2015; 31:312-9. [PMID: 25855285 DOI: 10.1051/medsci/20153103017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
The identification of complete networks of protein-protein interactions (PPI) within a cell has contributed to major breakthroughs in understanding biological pathways, host-pathogen interactions and cancer development. As a consequence, PPI have emerged as a new class of promising therapeutic targets. However, they are still considered as a challenging class of targets for drug discovery programs. Recent successes have allowed the characterization of structural and physicochemical properties of protein-protein interfaces leading to a better understanding of how they can be disrupted with small molecule compounds. In addition, characterization of the profiles of PPI inhibitors has allowed the development of PPI-focused libraries. In this review, we present the current efforts at developing chemical libraries dedicated to these innovative targets.
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Affiliation(s)
- Olivier Sperandio
- Molécules thérapeutiques in silico (MTi), université Paris Diderot, Inserm UMR-S973, 35, rue Hélène Brion, 75205 Paris Cedex 13, France
| | - Bruno O Villoutreix
- Molécules thérapeutiques in silico (MTi), université Paris Diderot, Inserm UMR-S973, 35, rue Hélène Brion, 75205 Paris Cedex 13, France
| | - Xavier Morelli
- Centre de recherche en cancérologie de Marseille (CRCM), CNRS UMR7258 ; Inserm U1068 ; institut Paoli-Calmettes ; université d'Aix-Marseille UM105, 27, boulevard Lei Roure,13009, Marseille, France
| | - Philippe Roche
- Centre de recherche en cancérologie de Marseille (CRCM), CNRS UMR7258 ; Inserm U1068 ; institut Paoli-Calmettes ; université d'Aix-Marseille UM105, 27, boulevard Lei Roure,13009, Marseille, France
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45
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Kuenemann MA, Sperandio O, Labbé CM, Lagorce D, Miteva MA, Villoutreix BO. In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 119:20-32. [PMID: 25748546 DOI: 10.1016/j.pbiomolbio.2015.02.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Revised: 02/18/2015] [Accepted: 02/24/2015] [Indexed: 12/22/2022]
Abstract
Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.
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Affiliation(s)
- Mélaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - Céline M Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France.
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46
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Flexibility and small pockets at protein-protein interfaces: New insights into druggability. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 119:2-9. [PMID: 25662442 PMCID: PMC4726663 DOI: 10.1016/j.pbiomolbio.2015.01.009] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 01/06/2015] [Accepted: 01/28/2015] [Indexed: 01/04/2023]
Abstract
The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein-protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein-protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of "orthosteric", competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein-protein interaction interfaces for which successful chemical modulators are being developed.
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47
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Harvey AL, Edrada-Ebel R, Quinn RJ. The re-emergence of natural products for drug discovery in the genomics era. Nat Rev Drug Discov 2015; 14:111-29. [PMID: 25614221 DOI: 10.1038/nrd4510] [Citation(s) in RCA: 1517] [Impact Index Per Article: 168.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.
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Affiliation(s)
- Alan L Harvey
- 1] Research and Innovation Support, Dublin City University, Dublin 9, Ireland. [2] Strathclyde Institute of Pharmacy and Biomedical Science, University of Strathclyde, Glasgow G4 0NR, UK
| | - RuAngelie Edrada-Ebel
- Strathclyde Institute of Pharmacy and Biomedical Science, University of Strathclyde, Glasgow G4 0NR, UK
| | - Ronald J Quinn
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia
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Ascher DB, Jubb HC, Pires DEV, Ochi T, Higueruelo A, Blundell TL. Protein-Protein Interactions: Structures and Druggability. MULTIFACETED ROLES OF CRYSTALLOGRAPHY IN MODERN DRUG DISCOVERY 2015. [DOI: 10.1007/978-94-017-9719-1_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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49
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Abstract
Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.
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Affiliation(s)
- Didier Rognan
- Laboratory for Therapeutical Innovation
- UMR7200 CNRS-Université de Strasbourg
- MEDALIS Drug Discovery Center
- 67400 Illkirch
- France
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50
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Ramakrishnan G, Chandra NR, Srinivasan N. From workstations to workbenches: Towards predicting physicochemically viable protein-protein interactions across a host and a pathogen. IUBMB Life 2014; 66:759-74. [DOI: 10.1002/iub.1331] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 11/06/2014] [Accepted: 11/16/2014] [Indexed: 01/03/2023]
Affiliation(s)
- Gayatri Ramakrishnan
- Indian Institute of Science Mathematics Initiative; Indian Institute of Science; Bangalore Karnataka India
- Molecular Biophysics Unit; Indian Institute of Science; Bangalore Karnataka India
| | - Nagasuma R. Chandra
- Department of Biochemistry; Indian Institute of Science; Bangalore Karnataka India
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